Abstract
Since DNA-dimer analysis has demonstrated to provide a very conserved pattern that has been suggested as a genome signature, in this paper we present a computational study of DNA-dimer distribution in a collection of Retroviral genomes. This analysis is based on two main steps: the generation of the target dataset, in this step, the DNA-dimer distribution variables are calculated and transformed to categorical data using Fuzzy Sets. And the induction of a Bayesian Network from the dataset. This induction technique is based on Genetic Algorithms. We have found interesting causal relationships between the DNA-dimer distribution variables and a set of chemical variables. These results could provide new directions in future Retroviral genomic investigations. The computational methodology presented in this paper has demonstrated to be an interesting tool for the study and the analysis of genomic sequences.
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© 2007 Springer-Verlag Berlin Heidelberg
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Garza-Domínguez, R., Quiroz-Gutiérrez, A. (2007). Analysis of DNA-Dimer Distribution in Retroviral Genomes Using a Bayesian Networks Induction Technique Based on Genetic Algorithms. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_107
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DOI: https://doi.org/10.1007/978-3-540-76631-5_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
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